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Update multilabel

pull/1265/head
cuicheng01 2021-09-27 03:00:37 +00:00
parent a90881c99f
commit fe6f614680
3 changed files with 9 additions and 9 deletions
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8
ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml
View File

@ -46,8 +46,8 @@ DataLoader:
Train: Train:
dataset: dataset:
name: MultiLabelDataset name: MultiLabelDataset
image_root: ./dataset/NUS-SCENE-dataset/images/ image_root: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/
cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_train_list.txt cls_label_path: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/multilabel_train_list.txt
transform_ops: transform_ops:
- DecodeImage: - DecodeImage:
to_rgb: True to_rgb: True
@ -74,8 +74,8 @@ DataLoader:
Eval: Eval:
dataset: dataset:
name: MultiLabelDataset name: MultiLabelDataset
image_root: ./dataset/NUS-SCENE-dataset/images/ image_root: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/
cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_test_list.txt cls_label_path: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/multilabel_test_list.txt
transform_ops: transform_ops:
- DecodeImage: - DecodeImage:
to_rgb: True to_rgb: True

2
ppcls/engine/evaluation/classification.py
View File

@ -50,7 +50,7 @@ def classification_eval(engine, epoch_id=0):
time_info["reader_cost"].update(time.time() - tic) time_info["reader_cost"].update(time.time() - tic)
batch_size = batch[0].shape[0] batch_size = batch[0].shape[0]
batch[0] = paddle.to_tensor(batch[0]).astype("float32") batch[0] = paddle.to_tensor(batch[0]).astype("float32")
if not evaler.config["Global"].get("use_multilabel", False): if not engine.config["Global"].get("use_multilabel", False):
batch[1] = batch[1].reshape([-1, 1]).astype("int64") batch[1] = batch[1].reshape([-1, 1]).astype("int64")
# image input # image input
out = engine.model(batch[0]) out = engine.model(batch[0])

8
ppcls/engine/train/train.py
View File

@ -76,8 +76,8 @@ def train_epoch(engine, epoch_id, print_batch_step):
tic = time.time() tic = time.time()
def forward(trainer, batch): def forward(engine, batch):
if not trainer.is_rec: if not engine.is_rec:
return trainer.model(batch[0]) return engine.model(batch[0])
else: else:
return trainer.model(batch[0], batch[1]) return engine.model(batch[0], batch[1])